Predictive modeling of ALS progression: an XGBoost approach using clinical features
Abstract This research presents a predictive model aimed at estimating the progression of Amyotrophic Lateral Sclerosis (ALS) based on clinical features collected from a dataset of 50 patients. Important features included evaluations of speech, mobility, and respiratory function. We utilized an XGBo...
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| Format: | Article |
| Language: | English |
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BMC
2024-12-01
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| Series: | BioData Mining |
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| Online Access: | https://doi.org/10.1186/s13040-024-00399-5 |
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| author | Richa Gupta Mansi Bhandari Anhad Grover Taher Al-shehari Mohammed Kadrie Taha Alfakih Hussain Alsalman |
| author_facet | Richa Gupta Mansi Bhandari Anhad Grover Taher Al-shehari Mohammed Kadrie Taha Alfakih Hussain Alsalman |
| author_sort | Richa Gupta |
| collection | DOAJ |
| description | Abstract This research presents a predictive model aimed at estimating the progression of Amyotrophic Lateral Sclerosis (ALS) based on clinical features collected from a dataset of 50 patients. Important features included evaluations of speech, mobility, and respiratory function. We utilized an XGBoost regression model to forecast scores on the ALS Functional Rating Scale (ALSFRS-R), achieving a training mean squared error (MSE) of 0.1651 and a testing MSE of 0.0073, with R² values of 0.9800 for training and 0.9993 for testing. The model demonstrates high accuracy, providing a useful tool for clinicians to track disease progression and enhance patient management and treatment strategies. |
| format | Article |
| id | doaj-art-d8c2674708d54ff4a0aed70e2781d6eb |
| institution | Kabale University |
| issn | 1756-0381 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BioData Mining |
| spelling | doaj-art-d8c2674708d54ff4a0aed70e2781d6eb2025-01-19T12:12:43ZengBMCBioData Mining1756-03812024-12-0117111110.1186/s13040-024-00399-5Predictive modeling of ALS progression: an XGBoost approach using clinical featuresRicha Gupta0Mansi Bhandari1Anhad Grover2Taher Al-shehari3Mohammed Kadrie4Taha Alfakih5Hussain Alsalman6Department of Computer Science and Engineering, School of Engineering Sciences and TechnologyDepartment of Computer Science and Engineering, School of Engineering Sciences and TechnologyDepartment of Computer Science and Engineering, School of Engineering Sciences and TechnologyComputer Skills, Department of Self-Development Skill, Common First Year Deanship, King Saud UniversityComputer Skills, Department of Self-Development Skill, Common First Year Deanship, King Saud UniversityDepartment of Information Systems, College of Computer and Information Sciences, King Saud UniversityDepartment of Computer Science, College of Computer and Information Sciences, King Saud UniversityAbstract This research presents a predictive model aimed at estimating the progression of Amyotrophic Lateral Sclerosis (ALS) based on clinical features collected from a dataset of 50 patients. Important features included evaluations of speech, mobility, and respiratory function. We utilized an XGBoost regression model to forecast scores on the ALS Functional Rating Scale (ALSFRS-R), achieving a training mean squared error (MSE) of 0.1651 and a testing MSE of 0.0073, with R² values of 0.9800 for training and 0.9993 for testing. The model demonstrates high accuracy, providing a useful tool for clinicians to track disease progression and enhance patient management and treatment strategies.https://doi.org/10.1186/s13040-024-00399-5Amyotrophic Lateral Sclerosis (ALS)ALS Functional Rating Scale (ALSFRS-R)Predictive modelingMachine learningDisease progressionXGBoost |
| spellingShingle | Richa Gupta Mansi Bhandari Anhad Grover Taher Al-shehari Mohammed Kadrie Taha Alfakih Hussain Alsalman Predictive modeling of ALS progression: an XGBoost approach using clinical features BioData Mining Amyotrophic Lateral Sclerosis (ALS) ALS Functional Rating Scale (ALSFRS-R) Predictive modeling Machine learning Disease progression XGBoost |
| title | Predictive modeling of ALS progression: an XGBoost approach using clinical features |
| title_full | Predictive modeling of ALS progression: an XGBoost approach using clinical features |
| title_fullStr | Predictive modeling of ALS progression: an XGBoost approach using clinical features |
| title_full_unstemmed | Predictive modeling of ALS progression: an XGBoost approach using clinical features |
| title_short | Predictive modeling of ALS progression: an XGBoost approach using clinical features |
| title_sort | predictive modeling of als progression an xgboost approach using clinical features |
| topic | Amyotrophic Lateral Sclerosis (ALS) ALS Functional Rating Scale (ALSFRS-R) Predictive modeling Machine learning Disease progression XGBoost |
| url | https://doi.org/10.1186/s13040-024-00399-5 |
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